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								<h2>SpeechBrain <br /></h2>
                <p>A PyTorch Powered Speech Toolkit</p>
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        			<h2>Key Features</h2>
        			<p>SpeechBrain is an open-source and all-in-one speech toolkit.
                It is designed to be simple, extremely flexible, and user-friendly. State-of-the-art performance are obtained in various domains.</p>
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        					<a href="#"><h4>Speech Recognition</h4></a>
                  <p>SpeechBrain supports state-of-the-art methods for end-to-end
                    speech recognition, including models based on CTC, CTC+attention, transducers, transformers,
                    and neural language models relying on recurrent neural networks and transformers.</p>
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        					<a href="#"><h4>Speaker Recognition</h4></a>
                  <p>Speaker recognition is already deployed in a wide variety of realistic applications.
                    SpeechBrain provides different models for speaker recognition, including X-vector, ECAPA-TDNN, PLDA, contrastive learning
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        					<a href="#"><h4>Speech Enhancement</h4></a>
                  <p>Spectral masking, spectral mapping, and time-domain enhancement are different methods already available within SpeechBrain.
                    Separation methods such as Conv-TasNet, DualPath RNN, and SepFormer are implemented as well.</p>
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        					<a href="#"><h4>Speech Processing</h4></a>
                  <p>SpeechBrain provides efficient and GPU-friendly speech augmentation pipelines and acoustic features extraction, normalisation
                    that can be used on-the-fly during your experiment.
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        					<a href="#"><h4>Multi Microphone Processing</h4></a>
        					<p>Combining multiple microphones is a powerful approach to achieve robustness in adverse acoustic environments.
                    SpeechBrain provides various techniques for beamforming (e.g, delay-and-sum, MVDR, and GeV) and speaker localization.

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        					<a href="#"><h4>Research & Development</h4></a>
                  <p>SpeechBrain is designed to speed-up research and development of speech technologies.
                    It is modular, flexible, easy-to-customize, and contains several recipes for popular datasets. Documentation and tutorials are here to
                    help newcomers using SpeechBrain.</p>
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        					<a href="#"><h4>HuggingFace!</h4></a>
                  <p>SpeechBrain provides multiple pre-trained models that can
                    easily be deployed with nicely designed interfaces.
                    Transcribing, verifying speakers, enhancing speech, separating sources have never been that easy! </p>
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        			<h2>Why SpeechBrain?</h2>
        			<p> SpeechBrain allows you to easily and quickly customize any part of your speech pipeline
                ranging from the data management up to the downstream task metric.<br/>
                No existing speech toolkit provides such a level of accessibility.</p>
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										<h3>Adapts to your needs.</h3>
										<h6>SpeechBrain allows users to install either via PyPI to rapidly use the standard library or via a local install to view recipes and further explore the features of the toolkit.</h6>
										<a class="main_btn" href="tutorial_basics.html">Get Started Now</a>
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										<pre><code class="bash">
  # From PyPI
  pip install speechbrain

  # Local installation
  git clone https://github.com/speechbrain/speechbrain.git
  cd speechbrain
  pip install -r requirements.txt
  pip install --editable .
                    </code></pre>
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										<h3>A single command.</h3>
										<h6>Every SpeechBrain recipe relies on a YAML file that summarizes all the functions and hyperparameters of the system.
                      A single Python script combines them to implement the desired task.</h6>
										<a class="main_btn" href="tutorial_basics.html">Get Started Now</a>
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										<pre><code class="bash">
  cd recipes/{dataset}/{task}/train

  # Train the model using the default recipe
  python train.py hparams/train.yaml

  # Train the model with a hyperparameter tweak
  python train.py hparams/train.yaml --learning_rate=0.1
                    </code></pre>
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  										<h3>Built for research.</h3>
  										<h6>SpeechBrain is designed for research and development. Hence, flexibility and transparency are
                      core concepts to facilitate our daily work. You can define your own deep learning models, losses, training / evaluation loops,
                      input pipeline / transformations and use them handily without overhead.
                      </h6>
  										<a class="main_btn" href="tutorial_basics.html">Get Started Now</a>
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  										<pre><code class="python">
  class ASR_Brain(sb.Brain):
    def compute_forward(self, batch, stage):

      # Compute features (mfcc, fbanks, etc.) on the fly
      feats = self.hparams.compute_feats(batch.wavs)

      # Improve robustness with pre-built augmentations
      feats = self.hparams.augment(feats)

      # Apply your custom model
      return self.modules.myCustomModel(feats)
                      </code></pre>
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        			<p>SpeechBrain thanks its generous sponsors. Sponsoring allows us to expand the team and further extend the functionalities of the toolkit.</p>
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        						<h3>About Us</h3>
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        					<p style="text-align:justify">SpeechBrain isn't a company or an association.
                    It is an open-source toolkit and a community created by Dr. Mirco Ravanelli and co-created by Dr. Titouan Parcollet.
                    We aim at making speech technologies more accessible for the community. </p>
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